A 3.4–3.6 GHz High-Selectivity Filter Chip Based on Film Bulk Acoustic Resonator Technology

The development of mobile 5G technology poses new challenges for high-frequency and high-performance filters. However, current commercial acoustic wave filters mainly focus on 4G LTE, which operates below 3 GHz. It is necessary to accelerate research on high-frequency acoustic wave filters. A high-selectivity film bulk acoustic resonator (FBAR) filter chip for the 3.4–3.6 GHz range was designed and fabricated in this paper. The design procedure includes FBAR parameter fitting, filter schematic analysis, and the generation principle of transmission zeros (TZs). The measured results show that the filter chip is of high roll-off and stopband suppression. Most of the stopband suppression is better than 35 dB. Finally, error analysis was conducted, and FBAR parameters were modified after testing for future filter design work

Structure and Electrochemical Properties of Mn3O4 Nanocrystal-Coated Porous Carbon Microfiber Derived from Cotton

Biomorphic Mn3O4 nanocrystal/porous carbon microfiber composites were hydrothermally fabricated and subsequently calcined using cotton as a biotemplate. The as-prepared material exhibited a specific capacitance of 140.8 F·g−1 at 0.25 A·g−1 and an excellent cycle stability with a capacitance retention of 90.34% after 5000 cycles at 1 A·g−1. These characteristics were attributed to the introduction of carbon fiber, the high specific surface area, and the optimized microstructure inherited from the biomaterial

Knowledge-based bidirectional thermal variable modelling for directed energy deposition additive manufacturing

Directed energy deposition additive manufacturing (DED-AM) has gained significant interest in producing large-scale metallic structural components. In this paper, a knowledge-based machine learning (ML) approach, combining both physics-based simulation and data-driven modelling, is proposed for a study on thermal variables of DED-AM. This approach enables both forward and backward predictions, which breaks down the barriers between the basic process parameters and key process attributes. Process knowledge plays a critical role to enable the prediction and enhance the accuracy in both prediction directions. The proposed ML approach successfully predicted the thermal variables of wire arc based DED-AM for forward modelling and the process parameters for backward modelling, typically within 7% errors. This approach can be further generalised as a powerful modelling tool for design, control, and evaluation of DED-AM processes regarding build geometry and properties, as well as an essential constituent element in a digital twin of a DED-AM system.Engineering and Physical Sciences Research CouncilThe authors would like to express their gratitude to Engineering and Physical Sciences Research Council (EPSRC) (EP/ R027218/1, New Wire Additive Manufacturing) for supporting aspects of this research.Virtual and Physical Prototypin

Prostate health index can stratify patients with Prostate Imaging Reporting and Data System score 3 lesions on magnetic resonance imaging to reduce prostate biopsies

We aim to evaluate prostate health index as an additional risk-stratification tool in patients with Prostate Imaging Reporting and Data System score 3 lesions on multiparametric magnetic resonance imaging. Men with biochemical or clinical suspicion of having prostate cancer who underwent multiparametric magnetic resonance imaging in two tertiary centers (Queen Mary Hospital and Princess Margaret Hospital, Hong Kong, China) between January 2017 and June 2022 were included. Ultrasound-magnetic resonance imaging fusion biopsies were performed after prostate health index testing. Those who only had Prostate Imaging Reporting and Data System score 3 lesions were further stratified into four prostate health index risk groups and the cancer detection rates were analyzed. Out of the 747 patients, 47.3% had Prostate Imaging Reporting and Data System score 3 lesions only. The detection rate of clinically significant prostate cancer in this group was 15.0%. The cancer detection rates of clinically significant prostate cancer had statistically significant differences: 5.3% in prostate health index <25.0, 7.4% in prostate health index 25.0–34.9, 17.9% in prostate health index 35.0–54.9, and 52.6% in prostate health index ≥55.0 (P < 0.01). Among the patients, 26.9% could have avoided a biopsy with a prostate health index <25.0, at the expense of a 5.3% risk of missing clinically significant prostate cancer. Prostate health index could be used as an additional risk stratification tool for patients with Prostate Imaging Reporting and Data System score 3 lesions. Biopsies could be avoided in patients with low prostate health index, with a small risk of missing clinically significant prostate cancer